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Assessing the quality of rules with a new monotonic interestingness measure z

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

The development of effective interestingness measures that help in interpretation and evaluation of the discovered knowledge is an active research area in data mining and machine learning. In this paper, we consider a new Bayesian confirmation measure for "if..., then..." rules proposed in [4]. We analyze this measure, called Z, with respect to valuable property M of monotonic dependency on the number of objects in the dataset satisfying or not the premise or the conclusion of the rule. The obtained results unveil interesting relationship between Z measure and two other simple and commonly used measures of rule support and anti-support, which leads to efficiency gains while searching for the best rules.
Original languageEnglish
Title of host publicationArtificial intelligence and soft computing: proceedings of the 9th international conference
EditorsL. Rutkowski, R. Tadeusiewicz, L. Zadeh, J. Zurada
Place of PublicationBerlin
Number of pages10
ISBN (Print)9783540695721
Publication statusPublished - 2008

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743

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